21 results about "Grid clustering" patented technology

The invention belongs to the technical field of equipment planning, and discloses an automatic planning method for intelligent agricultural Internet of Things equipment, which comprises the following steps: dividing a target region into grid units, collecting soil moisture parameters and nutrient parameters of each grid, and establishing a regional digital twinborn model; obtaining a target crop of each grid, obtaining growth information from the crop knowledge base, and executing time sequence simulation to generate a water and fertilizer demand table of each grid; executing a water-fertilizer balance strategy based on the water-fertilizer demand table, and generating a water-fertilizer supply plan of each grid; analyzing spatial distribution characteristics of supply plans, and clustering adjacent grids with similar plans into irrigation management partitions; and generating a pipe network topological structure and an equipment planning scheme based on the partition layout. According to the method, a traditional uniformly distributed area is scientifically divided into management subareas with consistent internal requirements, so that unified management of the same subarea is realized, and the problem of supply and demand mismatching caused by neglect of spatial heterogeneity in a traditional method is solved.

The invention discloses a trailer boundary dimension detection method based on unmanned container truck perception, and belongs to the technical field of automatic driving and vehicle engineering. In order to solve the problems of low manual correction efficiency and poor flexibility of external measurement equipment in the prior art, the invention provides the following scheme: acquiring three-dimensional point cloud information containing a vehicle through a vehicle-mounted laser radar, calculating a trailer rotation angle in real time by combining a high reflection intensity identifier, dynamically delimiting an interest region by using vehicle-mounted parameters, and filtering out a tractor point cloud; the remaining point clouds are distributed to a two-dimensional grid map, and candidate trailer point clouds are extracted through ground point cloud filtering and grid clustering analysis; finally, the maximum left width, the maximum right width, the maximum length and the maximum height of the trailer are calculated based on segmented time sequence checking, and the accurate boundary size is output. According to the method, manual intervention or external equipment support is not needed, real-time detection can be achieved under any trailer posture, and the safety and efficiency of unmanned container truck operation are effectively improved.

The invention discloses a sea route network construction method based on multistage grid clustering, and the method comprises the steps: setting a navigation region, collecting navigation data, carrying out the noise reduction, preprocessing and segmenting, and obtaining processed data; a space grid structure and a KD-tree structure are introduced on the basis of the DBSCAN algorithm to form a GBSC algorithm; a dynamic threshold mechanism is introduced on the basis of a Douglas-Peucker algorithm, and an adaptive DP algorithm is obtained; screening, clustering and correcting the trajectory segments and the trajectory points by using a GBSC algorithm and an adaptive DP algorithm to obtain a feature point set; a feature point closest to the track point is found in the feature point set through a KD-tree structure, a feature point sequence is obtained, repeated nodes are compressed to obtain a node sequence, adjacent nodes are connected, and an edge set is obtained to construct an adjacent matrix and an edge weight matrix to form an air route network; the method can truly reflect the actual navigation structure of the ship in the sea area, has a high coincidence degree with the original AIS trajectory, and significantly improves the accuracy and reliability of air route extraction.

The application discloses a weak coverage area grid clustering method and device, and the method comprises the following steps: performing slicing processing on a raster map to obtain a plurality of map slices; calculating the weak coverage probability of each map slice; screening the map slices to be clustered from the plurality of map slices according to the weak coverage probabilities of the plurality of map slices, performing clustering processing on the grids in the map slices to be clustered to obtain a weak coverage clustering cluster; collecting the update coverage data corresponding to the plurality of map slices, and updating the raster map according to the update coverage data and the weak coverage clustering cluster. The application realizes the management and processing of different slices by screening the map slices to be clustered, and does not need to perform complete traversal clustering and updating on the entire raster map, thereby saving the computing resource, effectively improving the update speed of the raster map, and solving the problems that the current clustering operation requires a large computing power and the real-time update map operation consumes a large amount of resources.

The invention discloses an image processing method. The method comprises the following steps: receiving a plurality of pieces of grid data of a component; transmitting the plurality of grid data to a GPU; clustering the plurality of pieces of grid data to obtain a plurality of pieces of grid clustering data; transmitting the plurality of grid clustering data to a GPU (Graphics Processing Unit); and the GPU is used for rendering the plurality of pieces of grid data and rendering the plurality of pieces of grid clustering data after receiving the plurality of pieces of grid clustering data. In the embodiment of the invention, since the grid data needs a certain time to be processed into the grid clustering data, the CPU can directly transmit a plurality of pieces of grid data to the GPU for rendering after receiving the grid data of one component, the GPU is prevented from being in an idle state in the process of generating the grid clustering data by the CPU, and the processing efficiency of the CPU is improved. Therefore, the screen cannot timely display the rendered image. After the GPU receives the grid clustering data, the grid data can be deleted, and then the grid clustering data is rendered, so that a patch after one component is dispersed can be added to a GPU video memory at one time.

The present invention relates to a bus route planning and visual analysis method for influencing factors based on GPS data. First, city taxi order data and city point of interest data are preprocessed to obtain passenger travel origin-destination data and POI category data. Passenger boarding and alighting hotspot grids are extracted, and candidate bus stops are mined based on grid clustering. Furthermore, a multi-objective model for bus route planning is constructed, and an optimal bus route set is generated through a multi-objective optimization method. Attribute weight quantification and ranking results are obtained based on statistical route attribute information. Finally, a visual analysis system supports planners in exploring and analyzing routes in the optimal route set and supports changing route attribute weights. The system retrains based on the adjusted attribute weights or subjective ranking to update the ranking results. Through interactive exploratory analysis, an optimal bus route planning solution is obtained, providing a scientific decision-making basis and practical means for transportation departments to carry out bus route planning, thereby facilitating the development of smart cities.

The present application relates to the technical field of wind power prediction, and discloses a canyon wind power short-term power prediction method based on grid clustering, comprising the following steps: step S1, according to the characteristics of canyon wind power, the short-term power prediction method is divided into a data cleaning link, a grid decomposition link and a power prediction link; step S2, in the data cleaning link, the historical meteorological data, wind power data and wind power prediction data learned by the LSTM network are subjected to anomaly detection, data correction and smoothing processing, and complete historical data are obtained; step S3, in the grid decomposition link, the regional grid clustering of the canyon wind power station is carried out according to the latitude, and the wind power and the characteristic grid associated with the meteorological data are obtained; step S4, in the power prediction link, the power of each grid is predicted by using the LSTM network, and power superposition and error correction are carried out. The present application is used to solve the problem of low accuracy of short-term wind power prediction caused by the influence of canyon wake on canyon wind power.

The invention provides an obstacle detection method and device, electronic equipment and a vehicle. The obstacle detection method comprises the steps of obtaining obstacle information around a vehicle and an occupied grid clustering object corresponding to the obstacle information; judging whether the object occupying the grid cluster is a large vehicle or not; if the occupied grid clustering object is a large vehicle and is in a turning scene, characteristic parameters of the occupied grid clustering object are acquired, and the characteristic parameters comprise a bending point of the occupied grid clustering object and a longest axis of the occupied grid clustering object; splitting the occupied grid clustering object according to the characteristic parameters to obtain a first part and a second part; and carrying out fusion processing on the first part and the second part to obtain a tracking obstacle, thereby realizing accurate detection on an unknown type of obstacle.

The application discloses a regional carbon emission data monitoring and analyzing system and method, and belongs to the technical field of big data analysis and traffic carbon emission monitoring. The method comprises the following steps: acquiring multi-source data of a city road network, wherein the multi-source data at least comprises traffic gate flow data, vehicle GPS track data, weather data and signal lamp timing data; dividing a city area into a plurality of spatial grid units; based on historical vehicle flow form indexes of each grid unit, combining preset business rules reflecting city functional area characteristics, performing dynamic clustering processing on the plurality of grid units to obtain a plurality of carbon emission characteristic grid clusters; wherein the judgment basis of the business rules at least partially originates from land use property data around the grid units; for each carbon emission characteristic grid cluster, according to the correlation characteristics of its vehicle flow form characteristics and event data, selecting a corresponding prediction algorithm to establish a traffic carbon emission prediction sub-model; based on the output of each sub-model, generating a carbon emission hotspot distribution map of the city road network in a future preset time period, and associating and outputting carbon emission contribution degrees and emission attribution analysis results of each characteristic grid cluster. Through the dynamic grid clustering mechanism of "vehicle flow form clustering + city functional area rule verification", the application realizes fine monitoring and accurate tracing of city traffic mobile source emissions, and effectively improves the spatio-temporal resolution and management operability of traffic carbon emission prediction.

The invention relates to the technical field of industrial production safety monitoring, in particular to a rolling bearing vibration signal prediction method based on an improved turnip optimization network model, and the method comprises the steps: obtaining a vibration signal of a target rolling bearing, and constructing the vibration signal into a time sequence; performing variational mode decomposition on the time sequence of the vibration signal to obtain a plurality of components; the component is input into a pre-constructed signal prediction model, a prediction result of the vibration signal is output, and the signal prediction model is constructed by optimizing a bidirectional gating circulation unit based on an improved kirschner optimization algorithm. The improved squirrel optimization algorithm is constructed by introducing a Bloch unit sphere coordinate quantum bit coding mechanism, a local grid clustering selection strategy, a predator flying-away strategy and an improved multi-difference Cauchy mutation strategy into the squirrel optimization algorithm. According to the method, the generalization ability of the model is enhanced through multi-strategy optimization, and the prediction precision and robustness of the vibration signal of the rolling bearing are remarkably improved.

The invention discloses a cross-modal sensing system and method for three-dimensional positioning and tracking of a high-speed dynamic object. The system comprises an event processor, a depth processor and a factor graph optimization module. The event processor analyzes the asynchronous event flow of the event camera in real time, and generates a target two-dimensional plane position through dynamic threshold filtering and grid clustering; a depth processor processes the data stream of the depth camera, generates an ROI through coordinate projection after receiving the two-dimensional position, and analyzes and extracts target depth information by using a depth histogram; the factor graph optimization module constructs a unified optimization framework containing an event projection residual factor, a depth measurement residual factor and a motion constraint factor, and the six-degree-of-freedom pose of the target is jointly optimized through maximum posteriori estimation. The event and depth processor adopts a parallel processing mechanism, realizes heterogeneous data tight coupling through a factor graph module, and can output a sub-millisecond-level low-delay three-dimensional positioning result in a high-speed dynamic scene.

The invention discloses a shaft part machining process automatic generation method and system based on a large model and view recognition. The system comprises a DXF file analysis module, a view recognition module, a feature extraction engine, a process knowledge base management module, a large model interaction and process generation module and a document automatic output module. Wherein the DXF file analysis module is used for reading and analyzing a drawing in a DXF format; the view recognition module is used for recognizing a plurality of views in a drawing and classifying primitive entities by adopting a hybrid algorithm based on grid clustering and rule judgment; the feature extraction engine is used for extracting geometric features and text features of the shaft parts; the process knowledge base management module is used for storing historical process data and providing case retrieval based on cosine similarity; the large model interaction and process generation module is used for driving a large language model to generate and optimize a processing process by constructing a structured cue word; and the document automatic output module is used for generating the standardized DOCX format process document. According to the method, the engineering drawing can be automatically analyzed, the features can be intelligently recognized, the high-quality machining process can be generated, automation and intelligence of process design are achieved, and the process design efficiency and quality are improved.

The present application belongs to the field of intelligent transportation technology, and in particular to a high-precision spatio-temporal trajectory recovery method based on cell phone signaling data, using a density clustering algorithm to pre-process original cell phone signaling data, using a grid clustering algorithm that fuses spatio-temporal features, introducing POI data from Baidu maps, and carrying out a clustering analysis from the spatio-temporal two dimensions, extracting a plurality of staying points of the signaling user, and then reconstructing a signaling user's travel trajectory based on the staying points, the method provided by the present application enables the user's reconstructed trajectory to have a higher degree of precision, and is more closely fitted to the user's real trajectory.

This disclosure presents a method and apparatus for processing non-uniform image data based on a visual language model. One specific implementation of the method includes: performing text box localization processing on the image to be processed to obtain a set of text box coordinate information; obtaining a density matrix; performing grid clustering processing on the density matrix to obtain an initial set of region bounding boxes; performing density adaptive expansion processing on the initial set of region bounding boxes to obtain a set of target region bounding boxes; performing multi-scale regional extraction processing on the image to be processed to obtain a global thumbnail and a set of local sub-images; performing fusion inference processing on the global thumbnail and the set of local sub-images to obtain a target content information set; performing differential compression processing on the image to be processed to obtain archived image data; and saving the archived image data to a storage medium. This implementation can reduce the lexical consumption of visual language model inference, reduce computational resource consumption, and reduce the storage space occupied by images.

The present application relates to the technical field of industrial production safety monitoring, and particularly relates to a rolling bearing vibration signal prediction method based on an improved kingfisher optimization network model, comprising: obtaining a vibration signal of a target rolling bearing and constructing a time series; performing variational mode decomposition on the time series of the vibration signal to obtain a plurality of components; inputting the components into a pre-constructed signal prediction model to output a prediction result of the vibration signal, wherein the signal prediction model is constructed based on an improved kingfisher optimization algorithm optimizing a bidirectional gate recurrent unit, and the improved kingfisher optimization algorithm is constructed by introducing a Bloch unit sphere coordinate quantum bit coding mechanism, a local grid clustering selection strategy, a fly-away predator strategy and an improved multi-difference Cauchy mutation strategy in the kingfisher optimization algorithm. The present application enhances the generalization ability of the model through multi-strategy optimization, and significantly improves the prediction accuracy and robustness of the rolling bearing vibration signal.

This invention discloses a social network user matching method based on grid clustering and spatiotemporal features. First, several pairs of users known to be the same user are obtained from the two social networks of the users to be matched, serving as seed user pairs. Negative example user pairs are then obtained from the seed user pairs and used as sample user pairs. Each user's check-in records are obtained, and qualified grid clusters for each user are acquired based on grid clustering. Spatiotemporal features are extracted from the user's check-in records, including user check-in stability, user check-in preference time, and user trajectory anchor points. The similarity of the qualified grid clusters, user check-in stability, user check-in preference time, and user trajectory anchor points of the user pairs are calculated. A user matching model is constructed and trained using the sample user pair data. The data of the user pairs to be matched is then input into the trained user matching model to obtain the matching results. This invention can achieve user matching even with imbalanced check-in data.

The application discloses a shared bicycle station site selection method based on density and in-out degree balance clustering, introduces an in-out balance coefficient, and performs clustering on a shared bicycle station candidate area through a composite clustering algorithm of the density and the balance coefficient; a GPS data set of the bicycle is taken as a sample point data set, the sample point data set is clustered by improving and combining the characteristics of a Clique grid clustering algorithm, a plurality of shared station candidate addresses are acquired, each shared station candidate address is optimized, and the best shared station address is determined by combining POI matching of the area. A Boolean expression is used to define complex attribute conditions in a formalized manner, and then a clustering result of the shared bicycle station under the complex attribute conditions is given based on the density and a threshold value. The application has high efficiency and accuracy, can perform data mining on massive user travel OD data, analyze hot spot gathering areas of bicycle user orders, provide good early data preparation for the bicycle station, and has the characteristics of lightness and flexibility.

Embodiments of the present disclosure disclose a non-uniform image data processing method and device based on a visual language model. A specific implementation of the method includes: performing text box positioning processing on a to-be-processed image to obtain a text box coordinate information set; obtaining a density matrix; performing grid clustering processing on the density matrix to obtain an initial region bounding box set; performing density adaptive expansion processing on the initial region bounding box set to obtain a target region bounding box set; performing multi-scale regionalization extraction processing on the to-be-processed image to obtain a global thumbnail and a local sub-image set; performing fusion inference processing on the global thumbnail and the local sub-image set to obtain a target content information set; performing differential compression processing on the to-be-processed image to obtain an archived image data, and saving the archived image data to a storage medium. The implementation can reduce the consumption of word units in visual language model inference, reduce the consumption of computing resources, and reduce the occupation of image pairs on storage space.

The present application relates to the technical field of cultural landscape division and management, and discloses a kind of country area cultural landscape unit division and management method, the method comprises: standardization country data set is extracted to country culture landscape element;Identify and demarcate cultural landscape gene patch, and the initial object patch layer is generated by carrying out segmentation processing to cultural landscape gene patch, the initial object patch layer is carried out weighted clustering analysis, and the cultural landscape unit partition sketch layer is obtained;The patch merging determination, partition optimization and boundary correction are carried out to the cultural landscape unit partition sketch layer, and the cultural landscape unit division result layer is obtained;Based on cultural landscape unit division result layer and comprehensive management and control level, matching control strategy realizes different management and control.The present application can effectively adapt to the characteristics of complex landscape in country area, compared with traditional grid clustering method, can better maintain the continuity of landscape pattern and cultural context, so that the scientificity and rationality of division result are more optimal.

The application discloses a map division method and device, a computer readable storage medium and a processor. The method comprises: obtaining a predetermined map of a predetermined area, wherein the predetermined map is used to show the building contour of the predetermined area, and the predetermined map comprises a plurality of preset grids, and the grid area of each preset grid is the same; determining the building density in each preset grid according to the building contour in each preset grid, wherein the building density is the ratio of the building area in the preset grid to the grid area, and the building area is determined according to the building contour; clustering the plurality of preset grids according to the building density to obtain a grid clustering result, wherein the grid clustering result comprises a plurality of density levels, and each density level comprises at least one preset grid; and dividing the predetermined map according to the grid clustering result. The application solves the technical problem of low planning efficiency caused by manual planning of the map.

The application discloses a kind of small target detection methods for unstructured road, comprising: input original point cloud data, utilize sensor installation parameter information, carry out point cloud ground filtering, obtain ground point cloud and non-ground point cloud;With ground point cloud and non-ground point cloud respectively construct ground point Map table and non-ground point Map table, complete operation is carried out to non-ground point cloud, grid clustering.In the vertical direction of the ground point Map table grid occupied by point cloud, construct voxel, PCA analysis;According to PCA analysis result, add neighborhood information to the grid containing obstacle;Iterate obstacle, utilize its neighborhood contained information to carry out neighborhood smoothness analysis, if it is ground characteristic neighborhood, then identify the obstacle as real obstacle;To the real obstacle identified, tracking is carried out, realizes the stable detection of obstacle, obtains final small target detection result.The method can significantly improve the detection precision of small target on unstructured road, improve the operation efficiency of unstructured road.